import cv2
import numpy as np
import pytesseract
import tensorflow as tf
from tensorflow.keras.preprocessing.image import img_to_array, load_img
from process import preprocess_image
from PIL import Image
from codecs import encode, decode
import requests

def infer_text(im):
    im.save("converted.png")
    url = "https://ajax.thehive.ai/api/demo/classify?endpoint=text_recognition"
    files = {
        "image": ("converted.png", open("converted.png", "rb"), "image/png"),
        "model_type": (None, "detection"),
        "media_type": (None, "photo"),
    }
    headers = {"referer": "https://thehive.ai/"}
    res = requests.post(url, headers=headers, files=files)
    text = ""
    for output in res.json()["response"]["output"]:
        text += output["block_text"]
    text = decode(encode(text, "latin-1", "backslashreplace"), "unicode-escape")
    return text

def find_order_id(uploaded_file, input_file, model, ocre):
    if ocre == 'Hive':
        uploaded_image = Image.open(uploaded_file)
        text = infer_text(uploaded_image)
    else:
        rotated = preprocess_image(uploaded_file)
        text = pytesseract.image_to_string(rotated)
        
    with input_file as file:
        file_contents = file.read().decode()
        lines = file_contents.split('\n')
        found = False
        possible_order_ids = []
        for line in lines:
            order_id, name, font = line.strip().split(',')
            if name.strip() in text:
                image = load_img(uploaded_file, target_size=(64, 64))
                image = img_to_array(image)
                image = np.expand_dims(image, axis=0)
                image = image / 255.0
                prediction = model.predict(image)
                font_type = 'Pacifico' if prediction[0, 0] > prediction[0, 1] else 'OpenSans-Light'
                if font_type == font.strip():
                    result = {
                        'status': 'success',
                        'message': f'Detected Text: {text.strip()}\n, Order ID: {order_id}, Predicted Font Type: {font_type}'
                    }
                    found = True
                    break
                else:
                    possible_order_ids.append(order_id)
        
        if not found:
            image = load_img(uploaded_file, target_size=(64, 64))
            image = img_to_array(image)
            image = np.expand_dims(image, axis=0)
            image = image / 255.0
            prediction = model.predict(image)
            font_type = 'Pacifico' if prediction[0, 0] > prediction[0, 1] else 'OpenSans-Light'
            for line in lines:
                order_id, name, font = line.strip().split(',')
                if font.strip() == font_type:
                    possible_order_ids.append(order_id)
            
            if len(possible_order_ids) > 0:
                result = {
                    'status': 'warning',
                    'message': f'Detected Text: {text.strip()}\n, Possible Order IDs: {",".join(possible_order_ids)}, Predicted Font Type: {font_type}'
                }
            else:
                result = {
                    'status': 'error',
                    'message': f'Detected Text: {text.strip()}\n, Could not find the Order ID and possible font matches.'
                }

    return result

def jaccard_similarity(s1, s2):
    set1 = set(s1.split())
    set2 = set(s2.split())
    intersection = len(set1.intersection(set2))
    union = len(set1.union(set2))
    return intersection / union

def find_order_id_similarity(uploaded_file, input_file, similarity_method, ocre):
    if ocre == 'Hive':
        uploaded_image = Image.open(uploaded_file)
        text = infer_text(uploaded_image)
    else:
        rotated = preprocess_image(uploaded_file)
        text = pytesseract.image_to_string(rotated)

    with input_file as file:
        file_contents = file.read().decode()
        lines = file_contents.split('\n')

        if similarity_method == 'exact_match':
            for line in lines:
                order_id, name, font = line.strip().split(',')
                if name.strip() == text.strip():
                    result = {
                        'status': 'success',
                        'message': f'Detected Text: {text.strip()}\n, Order ID: {order_id}'
                    }
                    return result
            message = f'Detected Text: {text.strip()}\n, Could not find the Order ID.'
            result = {'status': 'error', 'message': message}
            return result

        elif similarity_method == 'jaccard_similarity':
            possible_order_ids = []
            for line in lines:
                order_id, name, font = line.strip().split(',')
                jaccard_score = jaccard_similarity(name.strip(), text.strip())
                if jaccard_score >= 0.8:
                    result = {
                        'status': 'success',
                        'message': f'Detected Text: {text.strip()}\n, Order ID: {order_id}'
                    }
                    return result
                elif jaccard_score >= 0.5:
                    possible_order_ids.append(order_id)
            if len(possible_order_ids) > 0:
                message = f'Detected Text: {text.strip()}\n, Possible Order IDs: {",".join(possible_order_ids)}'
                result = {'status': 'warning', 'message': message}
                return result
            else:
                message = f'Detected Text: {text.strip()}\n, Could not find the Order ID.'
                result = {'status': 'error', 'message': message}
                return result

def find_order_id_2(uploaded_file, input_file, model, ocre):
    if ocre == 'Hive':
        uploaded_image = Image.open(uploaded_file)
        text = infer_text(uploaded_image)
    else:
        rotated = preprocess_image(uploaded_file)
        text = pytesseract.image_to_string(rotated)
        
    with input_file as file:
        file_contents = file.read().decode()
        lines = file_contents.split('\n')
        found = False
        possible_order_ids = []
        for line in lines:
            order_id, name, font = line.strip().split(',')
            if name.strip() in text:
                image = load_img(uploaded_file, target_size=(64, 64))
                image = img_to_array(image)
                image = np.expand_dims(image, axis=0)
                image = image / 255.0
                prediction = model.predict(image)
                
                class_names = ['Allibretto1.8.otf', 'Bella1.1.otf', 'Buffalo Nickel1.2.otf', 'Cervanttis1.18.otf', 'Claster1.6.otf', 'Fairy4.5.otf', 'Mon-Amour-April1.7.otf', 'Mon-Amour-Aug1.1.otf', 'Mon-Amour-Dec1.2.otf', 'Mon-Amour-Feb1.1.otf', 'Mon-Amour-January1.2.otf', 'Mon-Amour-July1.1.otf', 'Mon-Amour-June1.1.otf', 'Mon-Amour-Mar1.2.otf', 'Mon-Amour-May1.1.otf', 'Mon-Amour-Nov1.1.otf', 'Mon-Amour-Oct1.1.otf', 'Mon-Amour-Sept1.1.otf', 'Mon-Amour2.3.otf', 'Shelby1.3.otf', 'UKIJJ-Quill1.7.otf']
                predicted_class_index = np.argmax(prediction[0])
                predicted_class_name = class_names[predicted_class_index]
                
                if predicted_class_name.strip() == font.strip():
                    result = {
                        'status': 'success',
                        'message': f'Detected Text: {text.strip()}\n, Order ID: {order_id}, Predicted Font Type: {predicted_class_name.strip()}'
                    }
                    found = True
                    break
                else:
                    possible_order_ids.append(order_id)
        
        if not found:
            image = load_img(uploaded_file, target_size=(64, 64))
            image = img_to_array(image)
            image = np.expand_dims(image, axis=0)
            image = image / 255.0
            prediction = model.predict(image)
            
            class_names = ['Allibretto1.8.otf', 'Bella1.1.otf', 'Buffalo Nickel1.2.otf', 'Cervanttis1.18.otf', 'Claster1.6.otf', 'Fairy4.5.otf', 'Mon-Amour-April1.7.otf', 'Mon-Amour-Aug1.1.otf', 'Mon-Amour-Dec1.2.otf', 'Mon-Amour-Feb1.1.otf', 'Mon-Amour-January1.2.otf', 'Mon-Amour-July1.1.otf', 'Mon-Amour-June1.1.otf', 'Mon-Amour-Mar1.2.otf', 'Mon-Amour-May1.1.otf', 'Mon-Amour-Nov1.1.otf', 'Mon-Amour-Oct1.1.otf', 'Mon-Amour-Sept1.1.otf', 'Mon-Amour2.3.otf', 'Shelby1.3.otf', 'UKIJJ-Quill1.7.otf']
            predicted_class_index = np.argmax(prediction[0])
            predicted_class_name = class_names[predicted_class_index]
            
            for line in lines:
                order_id, name, font = line.strip().split(',')
                if font.strip() == predicted_class_name.strip():
                    possible_order_ids.append(order_id)
            
            if len(possible_order_ids) > 0:
                result = {
                    'status': 'warning',
                    'message': f'Detected Text: {text.strip()}\n, Possible Order IDs: {",".join(possible_order_ids)}, Predicted Font Type: {predicted_class_name.strip()}'
                }
            else:
                result = {
                    'status': 'error',
                    'message': f'Detected Text: {text.strip()}\n, Could not find the Order ID and possible font matches.'
                }

    return result